A diaphragm-free fiber-optic Fabry-Perot (FP) interferometric gas pressure sensor is designed and experimentally verified in this paper. The FP cavity was fabricated by inserting a well-cut fiber Bragg grating (FBG) and hollow silica tube (HST) from both sides into a silica casing. The FP cavity length between the ends of the SMF and HST changes with the gas density. Using temperature decoupling method to improve the accuracy of the pressure sensor in high temperature environments. An experimental system for measuring the pressure under different temperatures was established to verify the performance of the sensor. The pressure sensitivity of the FP gas pressure sensor is 4.28 nm/MPa with a high linear pressure response over the range of 0.1–0.7 MPa, and the temperature sensitivity is 14.8 pm/°C under the range of 20–800 °C. The sensor has less than 1.5% non-linearity at different temperatures by using temperature decoupling method. The simple fabrication and low-cost will help sensor to maintain the excellent features required by pressure measurement in high temperature applications.
Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.
The biphenylene network with periodically arranged four-, six-, and eightmembered rings has been successfully synthesized in very recent experiments. This novel two-dimensional (2D) carbon allotrope has potentials in applications of lithium storage and carbon-based circuitry. Understanding the thermal transport properties of biphenylene network is of critical importance for the performance and reliability of its practical applications. To this end, the thermal transport in biphenylene network is comprehensively investigated in this paper with the aid of molecular dynamics simulations together with first-principles calculations. For the sake of comparison, the thermal conductivities of other 2D sp2-hybridized carbon allotropes including graphene and pentaheptite are also investigated using the same method. It is found that the thermal conductivities of biphenylene network and pentaheptite are, respectively, only about one-thirteenth and one-eighth of graphene. Through the analysis of phonon property, mechanical property and electron density distribution, it is demonstrated that the great reduction in the thermal conductivity of biphenylene network and pentaheptite arises from the decline in their structural symmetry, which leads to the decrease of phonon group velocity and the reduction of phonon mean free path.
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